Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis
Name
s41598-017-17858-1.pdf
Size
2.48 MB
Format
Adobe PDF
Checksum (MD5)
ec53bbb43afef68c80b96ad2182d7332
Author(s)
Radhakrishnan, Adityanarayanan
Damodaran, Karthik
Soylemezoglu, Ali C.
Uhler, Caroline
Shivashankar, G. V.
Date Issued
December 2017
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Radhakrishnan, Adityanarayanan, Karthik Damodaran, Ali C. Soylemezoglu, Caroline Uhler, and G. V. Shivashankar. “Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis.” Scientific Reports 7, no. 1 (December 2017).
Version
Final published version
Abstract
Current cancer diagnosis employs various nuclear morphometric measures. While these have allowed accurate late-stage prognosis, early diagnosis is still a major challenge. Recent evidence highlights the importance of alterations in mechanical properties of single cells and their nuclei as critical drivers for the onset of cancer. We here present a method to detect subtle changes in nuclear morphometrics at single-cell resolution by combining fluorescence imaging and deep learning. This assay includes a convolutional neural net pipeline and allows us to discriminate between normal and human breast cancer cell lines (fibrocystic and metastatic states) as well as normal and cancer cells in tissue slices with high accuracy. Further, we establish the sensitivity of our pipeline by detecting subtle alterations in normal cells when subjected to small mechano-chemical perturbations that mimic tumor microenvironments. In addition, our assay provides interpretable features that could aid pathological inspections. This pipeline opens new avenues for early disease diagnostics and drug discovery.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41598-017-17858-1
